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Sebastian Nowozin, Microsoft Research Cambridge.
Tuesday 03 March 2015, 09:00-18:00
Microsoft Research Machine Learning and Perception Seminars
A series of seminars organized by the Machine Learning and Perception group at Microsoft Research in Cambridge.
0 upcoming talks View 64 archived talks
Workshop: Machine Learning in Cambridge 2015
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Bayesian Machine Learning for Controlling Autonomous Systems
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Compositionality in Vision and Language
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Foundations of Neuromechanical Systems Biology: Combining engineering, biology, and mathematics to understand how we move
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Strategies for General Recognition
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The cost of principles
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Lifts of Convex Sets and Cone Factorizations
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Efficient Multi-dimensional Parametric Mincuts for Constrained MAP Inference
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Noise estimation by PDE-constrained optimisation
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Building Better Questionnaires with Probabilistic Modelling
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Austerity in MCM - Land : Cutting the computational Budget
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Fast Variational Inference in the Conjugate Exponential Family
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Semantics\sqcap Scalability\models\bot? Are Semantics and Scalability Incompatible?
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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning
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New probabilistic methods for inference of natural selection on regulatory sequences in the human genome
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Daniel’s Adventures in Computer Vision Land
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From motion capture of interacting hands to video based rendering.
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Spectral Edge: Making the Invisible Visible
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Towards ad hoc interactions with robots
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From the Information Extraction Pipeline to Global Models, and Back
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Capture of dynamic scene using multiple cameras provides rich spatial-temporal information that can be used for solving challenging computer vision problems.
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Tell Me Where You’ve Lived and What You Want -- I’ll Tell You What You Like and Need
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Latent Hough Transform for Object Detection
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Context sensitive information: Which bits matter in data?
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SmartDesign: Living with Geometric Complexity
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Part A: Non-parametric image optimization & Part B: Crowdsourcing gaze data collection
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Multi-Label Learning with Millions of Categories & Generalized Multiple Kernel Learning with a Million Kernels
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Prediction Strategies without Loss
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The Inverted Multi-Index
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Topic Models for Human Activity Understanding
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ORCHID: Human-agent collectives for disaster response and the smart grid
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Sequential Decision Making in Experimental Design and Sustainability via Adaptive Submodularity
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Exploiting Variable Impedance for Robotics: Mimic or Optimize?
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Closing the gap between weakly and fully supervised methods
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Beyond the Piece of Cardboard: Learning to Adjust Photographs
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PARIS: Probabilistic Alignment of Relations, Instances, and Schema
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Probabilistic programs and the computability and complexity of Bayesian reasoning
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Exclusive Pólya Urns and their applications
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Information-Greedy Global Optimisation
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Mechanism Design without Money via Stable Matching
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SocialFusion: Fusing Mobile, Sensor, and Social Computing in the Cloud To Enable Next-Generation Context-Aware Applications
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Principles of Humanoid Locomotion Control
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Microsoft Research Cambridge, public talks
Machine Learning for WLAN Positioning
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